SPY Turn-of-the-Month Strategy
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Quant Buffet native backtest IDEEdit and run Quant Buffet Python for SPY Turn-of-the-Month Strategy in the browser. Results update live with equity, drawdown, and metrics charts. Allowed: backtest.data, backtest.engine, backtest.metrics, numpy, pandas. Define ASSETS and make_on_day(prices). Shortcut: Ctrl+Enter. API docs →
Quant Buffet syntax cheat sheet (copy / insert)
Paste these fragments into the editor. The sandbox rejects QuantConnect, os, and network libraries.
from __future__ import annotations
import numpy as np
import pandas as pd
from backtest.data import load_daily_prices
from backtest.engine import EngineConfig, PortfolioEngine
from backtest.metrics import compute_metricsASSETS = ["SPY", "QQQ", "TLT", "GLD", "BIL"]def make_on_day(prices: pd.DataFrame):
cols = [c for c in ASSETS if c in prices.columns]
sma = prices[cols].rolling(200, min_periods=200).mean()
state = {"last": None}
def on_day(engine: PortfolioEngine, dt: pd.Timestamp) -> None:
if sma.loc[dt].isna().all():
return
key = (dt.year, dt.month)
if state["last"] == key:
return
state["last"] = key
long = [
s for s in cols
if pd.notna(prices.at[dt, s]) and pd.notna(sma.at[dt, s])
and prices.at[dt, s] > sma.at[dt, s]
]
weights = {} if not long else {s: 1.0 / len(long) for s in long}
engine.set_target_weights(dt, weights)
ready = sma.dropna(how="all").index.min() if sma.notna().any().any() else None
return on_day, readyengine.set_target_weights(dt, {"SPY": 0.60, "BIL": 0.40})Live backtest performance
Export to your platform
Transform Quant Buffet lab code (ASSETS + make_on_day / PortfolioEngine) into native classes for a third-party IDE — then copy and paste.
# Generated from Quant Buffet → QuantConnect LEAN
# Strategy: SPY Turn-of-the-Month Strategy
# Detected pattern: Absolute momentum
# Source uses Quant Buffet lab APIs (ASSETS + make_on_day / PortfolioEngine).
# Review fees, data, and risk before live trading — educational export only.
from AlgorithmImports import *
class QuantBuffetExport(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
tickers = ["SPY", "TLT", "GLD", "BIL"]
self.symbols = []
for t in tickers:
if "-" in t: # crypto proxy e.g. BTC-USD
self.symbols.append(self.AddCrypto(t.replace("-USD", ""), Resolution.Daily).Symbol)
else:
self.symbols.append(self.AddEquity(t, Resolution.Daily).Symbol)
self.Schedule.On(
self.DateRules.MonthStart(self.symbols[0]),
self.TimeRules.AfterMarketOpen(self.symbols[0], 30),
self.Rebalance,
)
# Logic: Long assets with positive 252-day return; equal-weight; monthly.
def Rebalance(self):
# Pattern: abs_momentum — Long assets with positive 252-day return; equal-weight; monthly.
# Default: equal-weight. Port your make_on_day weights here via SetHoldings.
w = 1.0 / len(self.symbols) if self.symbols else 0.0
for symbol in self.symbols:
self.SetHoldings(symbol, w)
Exported code uses the platform’s native classes and libraries. Install dependencies in your third-party IDE, then run. Validate before live trading.
Academic paper
Wei Xu; John J. McConnell
- HSBC Holdings
- Purdue University West Lafayette
- Peking University
- ?HSBC School of Business, Peking University
- ?Purdue University
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=917884
Strategy in a nutshell
This strategy entails buying SPY ETF shares, which mimic the S&P 500, one day (or in some cases, four days) before a month ends and selling them on the close of the new month's third trading day. Aimed at exploiting the anticipated shifts in market sentiment and liquidity at month's end and start, this approach seeks to leverage potential short-term gains from these predictable patterns. Such a method requires precise timing for buy and sell orders, focusing on capturing slight, regular market movements rather than long-term investments, potentially offering an edge over traditional investment strategies.
Economic rationale
The turn-of-the-month anomaly, presenting higher stock returns around month-end, remains an academic conundrum. This phenomenon is observed across both small-cap, low-price and large-cap, high-price stocks, and is not exclusive to turn-of-the-year or quarter-end periods, challenging the notion that it could stem from higher risk or systematic shifts in interest rates. Interestingly, this pattern is not unique to the U.S., occurring in 30 markets worldwide. While Ogden (1990) suggested it might be linked to the timing of income receipts, pushing equity prices up as investors seek to deploy their funds, this theory has been contested. McConnell and Xu's work echoes the sentiment that the anomaly persists without a clear explanation. Some attribute it to monthly pension fund cash flows and the rebalancing of trading models, which could amplify the effect. Nevertheless, employing this strategy requires caution as such calendar effects may diminish or shift unpredictably over time.